Cargando…

Estimating three- and four-parameter MIRT models with importance-weighted sampling enhanced variational auto-encoder

Multidimensional Item Response Theory (MIRT) is widely used in educational and psychological assessment and evaluation. With the increasing size of modern assessment data, many existing estimation methods become computationally demanding and hence they are not scalable to big data, especially for th...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Tianci, Wang, Chun, Xu, Gongjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9421264/
https://www.ncbi.nlm.nih.gov/pubmed/36046415
http://dx.doi.org/10.3389/fpsyg.2022.935419
_version_ 1784777557705490432
author Liu, Tianci
Wang, Chun
Xu, Gongjun
author_facet Liu, Tianci
Wang, Chun
Xu, Gongjun
author_sort Liu, Tianci
collection PubMed
description Multidimensional Item Response Theory (MIRT) is widely used in educational and psychological assessment and evaluation. With the increasing size of modern assessment data, many existing estimation methods become computationally demanding and hence they are not scalable to big data, especially for the multidimensional three-parameter and four-parameter logistic models (i.e., M3PL and M4PL). To address this issue, we propose an importance-weighted sampling enhanced Variational Autoencoder (VAE) approach for the estimation of M3PL and M4PL. The key idea is to adopt a variational inference procedure in machine learning literature to approximate the intractable marginal likelihood, and further use importance-weighted samples to boost the trained VAE with a better log-likelihood approximation. Simulation studies are conducted to demonstrate the computational efficiency and scalability of the new algorithm in comparison to the popular alternative algorithms, i.e., Monte Carlo EM and Metropolis-Hastings Robbins-Monro methods. The good performance of the proposed method is also illustrated by a NAEP multistage testing data set.
format Online
Article
Text
id pubmed-9421264
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-94212642022-08-30 Estimating three- and four-parameter MIRT models with importance-weighted sampling enhanced variational auto-encoder Liu, Tianci Wang, Chun Xu, Gongjun Front Psychol Psychology Multidimensional Item Response Theory (MIRT) is widely used in educational and psychological assessment and evaluation. With the increasing size of modern assessment data, many existing estimation methods become computationally demanding and hence they are not scalable to big data, especially for the multidimensional three-parameter and four-parameter logistic models (i.e., M3PL and M4PL). To address this issue, we propose an importance-weighted sampling enhanced Variational Autoencoder (VAE) approach for the estimation of M3PL and M4PL. The key idea is to adopt a variational inference procedure in machine learning literature to approximate the intractable marginal likelihood, and further use importance-weighted samples to boost the trained VAE with a better log-likelihood approximation. Simulation studies are conducted to demonstrate the computational efficiency and scalability of the new algorithm in comparison to the popular alternative algorithms, i.e., Monte Carlo EM and Metropolis-Hastings Robbins-Monro methods. The good performance of the proposed method is also illustrated by a NAEP multistage testing data set. Frontiers Media S.A. 2022-08-15 /pmc/articles/PMC9421264/ /pubmed/36046415 http://dx.doi.org/10.3389/fpsyg.2022.935419 Text en Copyright © 2022 Liu, Wang and Xu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Liu, Tianci
Wang, Chun
Xu, Gongjun
Estimating three- and four-parameter MIRT models with importance-weighted sampling enhanced variational auto-encoder
title Estimating three- and four-parameter MIRT models with importance-weighted sampling enhanced variational auto-encoder
title_full Estimating three- and four-parameter MIRT models with importance-weighted sampling enhanced variational auto-encoder
title_fullStr Estimating three- and four-parameter MIRT models with importance-weighted sampling enhanced variational auto-encoder
title_full_unstemmed Estimating three- and four-parameter MIRT models with importance-weighted sampling enhanced variational auto-encoder
title_short Estimating three- and four-parameter MIRT models with importance-weighted sampling enhanced variational auto-encoder
title_sort estimating three- and four-parameter mirt models with importance-weighted sampling enhanced variational auto-encoder
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9421264/
https://www.ncbi.nlm.nih.gov/pubmed/36046415
http://dx.doi.org/10.3389/fpsyg.2022.935419
work_keys_str_mv AT liutianci estimatingthreeandfourparametermirtmodelswithimportanceweightedsamplingenhancedvariationalautoencoder
AT wangchun estimatingthreeandfourparametermirtmodelswithimportanceweightedsamplingenhancedvariationalautoencoder
AT xugongjun estimatingthreeandfourparametermirtmodelswithimportanceweightedsamplingenhancedvariationalautoencoder